Price Regulation, Quality Competition and Location Choice with Costly Relocation - Hehenkamp, Burkhard; Kaarbøe, Oddvar M.
We consider a model where for-profit providers compete in quality in a price-regulated market that has been opened to competition, and where the incumbent is located at the center of the market, facing high costs of relocation. The model is relevant in markets such as public health care, education and schooling, or postal services. We find that, when the regulated price is low or intermediate, the entrant strategically locates towards the corner of the market to keep the incumbent at the low monopoly quality level. For a high price, the entrant locates at the corner of the market and both providers implement higher quality compared to a monopoly. In any case, the entrant implements higher quality than the incumbent provider. Social welfare is always higher in a duopoly if the cost of quality is low. For higher cost levels welfare is non-monotonic in the price and it can be optimal to the regulator not to use its entire budget. Therefore, the welfare effect of entry depends on the price and the size of the entry cost, and the regulator should condition the decision to allow entry on an assessment of the entry cost.
Keywords: Quality competition; Price regulation; Location choice; Product differentiation

Distribution Network Optimization: Predicting computation times to design scenario analysis for network operators - Burmeister, Sascha Christian; Schryen, Guido (Energy Systems)

Business Models for Cultural Event Platforms – A Taxonomy Approach - Althaus, Maike; Müller, Michelle; Vorbohle, Christian; Kundisch, Dennis (Proceedings of the International Conference on Challenges in Managing Smart Products and Services (CHIMSPAS 2023))

May I have your Attention, please? – The Interaction between Attention Screening and Reputation on Crowdworking Platforms - Laux, Florian; Poniatowski, Martin; Kundisch, Dennis (Proceedings of the 31st European Conference on Information Systems (ECIS))

Designing Business Reputation Ecosystems — A Method for Issuing and Trading Monetary Ratings on a Blockchain - Hemmrich, Simon; Bobolz, Jan; Beverungen, Daniel; Blömer, Johannes (ECIS 2023 Research Papers)
Market transactions are subject to information asymmetry about the delivered value proposition, causing transaction costs and adverse market effects among buyers and sellers. Information systems research has investigated how review systems can reduce information asymmetry in business-to-consumer markets. However, these systems cannot be readily applied to business-to-business markets, are vulnerable to manipulation, and suffer from conceptual weak spots since they use textual data or star ratings. Building on design science research, we conceptualize a new class of reputation systems based on monetary-based payments as quantitative ratings for each transaction stored on a blockchain. Using cryptography, we show that our system assures content confidentiality so that buyers can share and sell their ratings selectively, establishing a reputation ecosystem. Our prescriptive insights advance the design of reputation systems and offer new paths to understanding the antecedents, dynamics, and consequences to reduce information asymmetry in B2B transactions.

Supporting Innovation through B2B Reviews – A Taxonomy of B2B Online Review Environments - Seutter, Janina; Bartelheimer, Christian; Kundisch, Dennis (Proceedings of the 18th International Conference on Design Science Research in Information Systems and Technology)

Updating at the Expense of Demand? The Case of Platform Apps - Jabr, Wael; Gutt, Dominik; Neumann, Jürgen; Kundisch, Dennis (Proceedings of the 45th ISMS Marketing Science Conference)

HIEF: a holistic interpretability and explainability framework - Kucklick, Jan-Peter (Journal of Decision Systems)
Many applications are driven by Machine Learning (ML) today. While complex ML models lead to an accurate prediction, their inner decision-making is obfuscated. However, especially for high-stakes decisions, interpretability and explainability of the model are necessary. Therefore, we develop a holistic interpretability and explainability framework (HIEF) to objectively describe and evaluate an intelligent system’s explainable AI (XAI) capacities. This guides data scientists to create more transparent models. To evaluate our framework, we analyse 50 real estate appraisal papers to ensure the robustness of HIEF. Additionally, we identify six typical types of intelligent systems, so-called archetypes, which range from explanatory to predictive, and demonstrate how researchers can use the framework to identify blind-spot topics in their domain. Finally, regarding comprehensiveness, we used a random sample of six intelligent systems and conducted an applicability check to provide external validity.
Keywords: Explainable AI (XAI); machine learning; interpretability; real estate appraisal; framework; taxonomy

Digitale Transformation im Theater – Mittels Besucherforschung und Entscheidungsunterstützung zur besseren Angebotsgestaltung - Kempkes, Jens Peter; Kreuzhage, Katharina; Kundisch, Dennis; Seutter, Janina; Weskamp, Christoph

Crowdwork in the Age of Algorithms – How Algorithmic Requesters affect the Meaningfulness of Crowdwork - Laux, Florian; Haff, André; Kundisch, Dennis